[Epidemioloical characteristics and economic burden analysis of palmoplantar pustulosis in urban areas of China].

Objective: To analyze the epidemiological characteristics and economic burden of palmoplantar pustulosis (PPP) in China. Methods: A population-based retrospective study was conducted using the data from China's Urban Basic Medical Insurance data from January 1, 2012, to December 31, 2016. International Classification of Diseases code and diagnoses in Chinese for PPP were used to identify cases and estimate the prevalence, incidence, and cost. Subgroup analyses were performed according to age and sex, and sensitivity analyses were conducted to evaluate the robustness of the results. Age-adjusted prevalence rates were calculated based on the 2010 national census data. Results: The crude prevalence and incidence rate of PPP in 2016 were 2.730/100 000 (95%CI: 2.218/100 000-3.242/100 000) and 1.556/100 000 (95%CI: 1.154/100 000-1.958/100 000), and the prevalence rate of females (2.910/100 000) was higher than that of males (2.490/100 000, χ2=97.48, P=0.001). The incidence rate of females (1.745/100 000) was also higher than that of males (1.418/100 000, χ2=85.02, P=0.001). The age peak of incidence and prevalence of patients with PPP was in the 30-39-year age group and a small peak existed in the 0-3-year age group among people under 20 years old. From 2012 to 2016, the average number of visits was (2.44±0.04) per patient, and the total per-capita cost per year was (982.40±39.19) yuan. Conclusion: In 2016, the prevalence and incidence rate of PPP in China were higher in females than in males, and the highest age peak was in the 30-39-year age group.

[Prognostic value of skeletal muscle measured by CT at the T4 level in advanced EGFR-positive non-small cell lung cancer patients treated with ecotinib].

Objective: To investigate the prognostic value of skeletal muscle measured by CT at the level of the fourth thoracic vertebra (T4) in advanced epidermal growth factor receptor (EGFR)-positive non-small cell lung cancer (NSCLC) patients treated with ecotinib. Methods: The study retrospectively reviewed clinical and pathological characteristics of 176 patients with advanced EGFR-positive NSCLC who received ecotinib and underwent chest CT scans at Wuhan Union Hospital between January 2017 and October 2020. Among them, 70 were male and 106 were female, with ages ranging from 27 to 80 (58.6±10.6) years. As of August 21, 2021, the median follow-up duration was 19.2 months (95%CI: 15.3 to 23.7 months). The optimal cut-off value of skeletal muscle density (T4-SMD) on CT images at the T4 level were determined using X-tile software. Kaplan-Meier analysis and log-rank test were used to plot progression-free survival curves. Cox proportional hazards regression models were employed to analyze factors influencing 1-year progression-free survival (PFS), and a nomogram prognostic model was constructed accordingly. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were utilized to evaluate the predictive value of the nomogram. Results: The T4-SMD [M (Q1,Q3)] of 176 patients was 42.56 (37.05, 45.93) HU. Patients were divided into low T4-SMD group (n=122) and high T4-SMD group (n=54) based on the cut-off value (The values for males and females were 49.44 and 41.41 HU, respectively) of T4-SMD. The median PFS time and 1-year PFS rate in the low T4-SMD group were significantly lower than those in the high T4-SMD group [10.4 (95%CI: 9.3-11.8) vs 13.7 (95%CI: 11.1-18.5) months, 36.1% vs 59.3%, respectively, P=0.034]. Eastern Cooperative Oncology Group performance status (HR=3.308, 95%CI: 1.183-9.247, P=0.023), lactate dehydrogenase level (HR=1.852, 95%CI: 1.037-3.307, P=0.037), systemic immune-inflammation index (HR=1.772, 95%CI: 1.019-3.080, P=0.043), and T4-SMD (HR=0.563, 95%CI: 0.325-0.974, P=0.040) were prognostic factors for 1-year PFS in advanced EGFR-positive NSCLC patients treated with ecotinib. A nomogram for predicting 1-year PFS of advanced EGFR-positive NSCLC patients treated with ecotinib was constructed based on the four indicators selected by multivariate Cox regression analysis. The area under the ROC curve of the nomogram was 0.775 (95%CI: 0.676-0.874). The calibration curve showed good consistency between the predicted and actual 1-year PFS. DCA demonstrated good clinical prediction effectiveness of the nomogram. Conclusion: Low T4-SMD is a prognostic risk factor for patients with advanced EGFR-positive NSCLC receiving icotinib therapy.

[Application and advancement of digital teaching materials in teaching epidemiology].

This study aims at examining the application and development of digital teaching materials in the field of epidemiology, encompassing both China and international contexts. The research involved conducting search on websites and literature databases to assess the status of digital teaching materials in epidemiology, nationally and internationally. At present, in China, digital teaching materials used in epidemiology are primarily presented in the form of printed books with added QR codes, providing teaching resources such as videos and exercises. However, issues with the level of interactivity have been identified. In foreign countries, with stronger emphasis placed on personalization, interactivity, and the use of rich media technologies in the digital teaching materials, epidemiologically. Enhanced digitization regarding materials and learning outcomes is achieved through features such as real-time notes, interactive animations, and quizzes. These approaches are considered worth considering for adoption. This study provides valuable insights for the digital transformation of epidemiology education.

[Automated cephalometric landmark identification and location based on convolutional neural network].

Objective: To develop an automated landmark location system applicable to the case of landmark missing. Methods: Four and eighty-one lateral cephalograms, which contained 240 males and 241 females, with an average age of (24.5±5.6) years, taken from January 2015 to January 2021 in the Department of Orthodontics, Capital Medical University School of Stomatology, and met the inclusion criteria were collected. Five postgraduate orthodontic students were the annotators to manually locate 61 possible landmarks in 481 lateral cephalograms. Two assistant professors in the department as reviewers performed calibration. Two professors as arbitrators, made final decision. Data sets were established (341 were used as training set, 40 as validation set, and 100 as test set). In this paper, an automatic landmarks identification and location model based on convolutional neural networks (CNN), CephaNET, was developed. The model was trained by feeding the original image into the feature extraction module and convolutional pose machine (CPM) module to locate landmarks with high accuracy using deep supervision. Training set was enhanced to 1 684 images by histogram equalization, cropping, and adjustment of brightness. The model was trained to compare the Gaussian heat maps output from the network with the set threshold to identify landmark missing cases. Test set of 100 lateral cephalograms was used to test the accuracy of the model. The evaluation criteria used were success detection rate of missing landmark, mean radial error (MRE) and success detection rate (SDR) in the range of 2.0, 2.5, 3.0, 3.5 and 4.0 mm. Results: The model identified and located 61 commonly used landmarks in 0.13 seconds on average. It had an average accuracy of 93.5% in identifying missing landmarks. The MRE of our testing set was (1.19±0.91) mm. SDR of 2.0, 2.5, 3.0, 3.5 and 4.0 mm were 85.4%, 90.2%, 93.5%, 95.4%, 97.0% respectively. Conclusions: The model proposed in this paper could adapt to the absence of landmark in lateral cephalograms and locate 61 commonly used landmarks with high accuracy to meet the requirements of different cephalometric analysis methods.

An E-band multi-channel Doppler backscattering system on EAST.

An E-band (60-90 GHz) multi-channel Doppler backscattering (DBS) system with X-mode polarization has been installed on the Experimental Advanced Superconducting Tokamak (EAST), which can measure the turbulence at five different radial locations simultaneously. This system can launch 31 fixed microwave frequencies in the range of 60-90 GHz with a 1 GHz interval into the plasma, and five probing signals are selected by employing a reference signal and multiple filters. During experiments, the frequency of the reference signal is tunable in the E-band, and the selected probing signals can be changed as needed without any other adjustments, which can be performed in one shot or between shots. Furthermore, the incident angle can be adjusted from -10° to 20°, and the wavenumber range is 4-25 cm-1 with a wavenumber resolution of Δk/k ≤ 0.35. Ray tracing simulations are employed to calculate the scattering locations and the perpendicular wavenumber. In this article, the hardware design, ray tracing, and initial results obtained from the EAST plasma will be presented.

[Methodology and progress in adjusting time-dependent covariates in clinical prediction models].

Adjusting time-dependent covariates into prediction models may help improve model performance and expand clinical applications. The methodology of handling time-dependent covariates is limited in traditional regression strategies (i.e., landmark model, joint model). For example, the number of predictors and practical situations which can be handled are restricted when using regression models. One new strategy is to use machine learning (i.e., neural networks). This review summarizes the methodology of handling time-dependent covariates in prediction models, such as applicable scenarios, strengths, and limitations, to offer methodological enlightenment for processing time-dependent covariates.

[Progress in research of association between phosphodiesterase 5 inhibitors and cancer incidence].

With the increased use of phosphodiesterase 5 inhibitors (PDE5Is) over the past years, several population based studies have suggested a potential association between PDE5Is and the risk for specific cancers. This paper systematically summarizes the current status of relatedstudies. Meta-analyses on current research indicated that the use of PDE5Is might be associated with the increased risk for melanoma (RR=1.11, 95%CI: 1.02-1.22) and basal cell carcinoma (RR=1.16, 95%CI: 1.13-1.20), but not for prostate cancer (OR=0.71, 95%CI: 0.40-1.29), and might have chemoprophylaxis effect on colorectal cancer (RR=0.85, 95%CI: 0.76-0.95). However, results of squamous cell carcinoma were still inconsistent. Further exploration based on the experience and limitations of current research is suggested.

[Genetic characteristics of the first human infection with the G4 genotype eurasian avian-like H1N1 swine influenza virus in Shaanxi Province,China].

Objective: To analyze the genetic characteristics of the first human infection with the G4 genotype of Eurasian avian H1N1 swine influenza virus (EA H1N1 SIV) in Shaanxi Province. Methods: The patient's throat swab samples were collected, and MDCK cells were inoculated for virus isolation to obtain the virus strain. The whole genome deep sequencing method was used to obtain the eight gene segments of the isolated strain. The nucleotide homology analysis was conducted through the Blast program in the GenBank database, and a phylogenetic tree was constructed to analyze the genetic characteristics of the virus. Results: The throat swab specimens of the case were confirmed as EA H1N1 SIV in the laboratory, and the isolated strain was named A/Shaanxi-Weicheng/1351/2022(H1N1v). Homology analysis found that the PB2, NP, HA, NA, and M genes of this isolate had the highest nucleotide homology with A/swing/Beijing/0301/2018 (H1N1), about 98.29%, 98.73%, 97.41%, 97.52%, and 99.08%, respectively. The phylogenetic tree showed that the isolate belonged to G4 genotype EA H1N1 SIV, with PB2, PB1, PA, NP and M genes from pdm/09 H1N1, HA and NA genes from EA H1N1, and NS gene from Triple-reassortant H1N1. The cleavage site of the HA protein was IPSIQSR↓G, which was the molecular characteristic of the low pathogenic influenza virus. No amino acid mutations associated with neuraminidase inhibitors were found in the NA protein. PB2 protein 701N mutation, PA protein P224S mutation, NP protein Q357K mutation, M protein P41A mutation, and NS protein 92D all indicated its enhanced adaptability to mammals. Conclusion: The patient is the first human infection with G4 genotype EA H1N1 SIV in Shaanxi province. The virus is low pathogenic, but its adaptability to mammals is enhanced. Therefore, it is necessary to strengthen the monitoring of such SIVs.

[Development and validation of risk prediction model for new-onset cardiovascular diseases among breast cancer patients: Based on regional medical data of Inner Mongolia].

OBJECTIVE: To develop and validate a three-year risk prediction model for new-onset cardiovascular diseases (CVD) among female patients with breast cancer. METHODS: Based on the data from Inner Mongolia Regional Healthcare Information Platform, female breast cancer patients over 18 years old who had received anti-tumor treatments were included. The candidate predictors were selected by Lasso regression after being included according to the results of the multivariate Fine & Gray model. Cox proportional hazard model, Logistic regression model, Fine & Gray model, random forest model, and XGBoost model were trained on the training set, and the model performance was evaluated on the testing set. The discrimination was evaluated by the area under the curve (AUC) of the receiver operator characteristic curve (ROC), and the calibration was evaluated by the calibration curve. RESULTS: A total of 19 325 breast cancer patients were identified, with an average age of (52.76±10.44) years. The median follow-up was 1.18 [interquartile range (IQR): 2.71] years. In the study, 7 856 patients (40.65%) developed CVD within 3 years after the diagnosis of breast cancer. The final selected variables included age at diagnosis of breast cancer, gross domestic product (GDP) of residence, tumor stage, history of hypertension, ischemic heart disease, and cerebrovascular disease, type of surgery, type of chemotherapy and radiotherapy. In terms of model discrimination, when not considering survival time, the AUC of the XGBoost model was significantly higher than that of the random forest model [0.660 (95%CI: 0.644-0.675) vs. 0.608 (95%CI: 0.591-0.624), P < 0.001] and Logistic regression model [0.609 (95%CI: 0.593-0.625), P < 0.001]. The Logistic regression model and the XGBoost model showed better calibration. When considering survival time, Cox proportional hazard model and Fine & Gray model showed no significant difference for AUC [0.600 (95%CI: 0.584-0.616) vs. 0.615 (95%CI: 0.599-0.631), P=0.188], but Fine & Gray model showed better calibration. CONCLUSION: It is feasible to develop a risk prediction model for new-onset CVD of breast cancer based on regional medical data in China. When not considering survival time, the XGBoost model and the Logistic regression model both showed better performance; Fine & Gray model showed better performance in consideration of survival time.

[Automated diagnostic classification with lateral cephalograms based on deep learning network model].

Objective: To establish a comprehensive diagnostic classification model of lateral cephalograms based on artificial intelligence (AI) to provide reference for orthodontic diagnosis. Methods: A total of 2 894 lateral cephalograms were collected in Department of Orthodontics, Capital Medical University School of Stomatology from January 2015 to December 2021 to construct a data set, including 1 351 males and 1 543 females with a mean age of (26.4± 7.4) years. Firstly, 2 orthodontists (with 5 and 8 years of orthodontic experience, respectively) performed manual annotation and calculated measurement for primary classification, and then 2 senior orthodontists (with more than 20 years of orthodontic experience) verified the 8 diagnostic classifications including skeletal and dental indices. The data were randomly divided into training, validation, and test sets in the ratio of 7∶2∶1. The open source DenseNet121 was used to construct the model. The performance of the model was evaluated by classification accuracy, precision rate, sensitivity, specificity and area under the curve (AUC). Visualization of model regions of interest through class activation heatmaps. Results: The automatic classification model of lateral cephalograms was successfully established. It took 0.012 s on average to make 8 diagnoses on a lateral cephalogram. The accuracy of 5 classifications was 80%-90%, including sagittal and vertical skeletal facial pattern, mandibular growth, inclination of upper incisors, and protrusion of lower incisors. The acuracy rate of 3 classifications was 70%-80%, including maxillary growth, inclination of lower incisors and protrusion of upper incisors. The average AUC of each classification was ≥0.90. The class activation heat map of successfully classified lateral cephalograms showed that the AI model activation regions were distributed in the relevant structural regions. Conclusions: In this study, an automatic classification model for lateral cephalograms was established based on the DenseNet121 to achieve rapid classification of eight commonly used clinical diagnostic items.

[Research on multi-class orthodontic image recognition system based on deep learning network model].

Objective: To develop a multi-classification orthodontic image recognition system using the SqueezeNet deep learning model for automatic classification of orthodontic image data. Methods: A total of 35 000 clinical orthodontic images were collected in the Department of Orthodontics, Capital Medical University School of Stomatology, from October to November 2020 and June to July 2021. The images were from 490 orthodontic patients with a male-to-female ratio of 49∶51 and the age range of 4 to 45 years. After data cleaning based on inclusion and exclusion criteria, the final image dataset included 17 453 face images (frontal, smiling, 90° right, 90° left, 45° right, and 45° left), 8 026 intraoral images [frontal occlusion, right occlusion, left occlusion, upper occlusal view (original and flipped), lower occlusal view (original and flipped) and coverage of occlusal relationship], 4 115 X-ray images [lateral skull X-ray from the left side, lateral skull X-ray from the right side, frontal skull X-ray, cone-beam CT (CBCT), and wrist bone X-ray] and 684 other non-orthodontic images. A labeling team composed of orthodontic doctoral students, associate professors, and professors used image labeling tools to classify the orthodontic images into 20 categories, including 6 face image categories, 8 intraoral image categories, 5 X-ray image categories, and other images. The data for each label were randomly divided into training, validation, and testing sets in an 8∶1∶1 ratio using the random function in the Python programming language. The improved SqueezeNet deep learning model was used for training, and 13 000 natural images from the ImageNet open-source dataset were used as additional non-orthodontic images for algorithm optimization of anomaly data processing. A multi-classification orthodontic image recognition system based on deep learning models was constructed. The accuracy of the orthodontic image classification was evaluated using precision, recall, F1 score, and confusion matrix based on the prediction results of the test set. The reliability of the model's image classification judgment logic was verified using the gradient-weighted class activation mapping (Grad-CAM) method to generate heat maps. Results: After data cleaning and labeling, a total of 30 278 orthodontic images were included in the dataset. The test set classification results showed that the precision, recall, and F1 scores of most classification labels were 100%, with only 5 misclassified images out of 3 047, resulting in a system accuracy of 99.84%(3 042/3 047). The precision of anomaly data processing was 100% (10 500/10 500). The heat map showed that the judgment basis of the SqueezeNet deep learning model in the image classification process was basically consistent with that of humans. Conclusions: This study developed a multi-classification orthodontic image recognition system for automatic classification of 20 types of orthodontic images based on the improved SqueezeNet deep learning model. The system exhibitted good accuracy in orthodontic image classification.

[A descriptive analysis on hypertension in adult twins in China].

Objective: To describe the distribution characteristics of hypertension among adult twins in the Chinese National Twin Registry (CNTR) and to provide clues for exploring the role of genetic and environmental factors on hypertension. Methods: A total of 69 220 (34 610 pairs) of twins aged 18 and above with hypertension information were selected from CNTR registered from 2010 to 2018. Random effect models were used to describe the population and regional distribution of hypertension in twins. To estimate the heritability, the concordance rates of hypertension were calculated and compared between monozygotic twins (MZ) and dizygotic twins (DZ). Results: The age of all participants was (34.1±12.4) years. The overall self-reported prevalence of hypertension was 3.8%(2 610/69 220). Twin pairs who were older, living in urban areas, married, overweight or obese, current smokers or ex-smokers, and current drinkers or abstainers had a higher self-reported prevalence of hypertension (P<0.05). Analysis within the same-sex twin pairs found that the concordance rate of hypertension was 43.2% in MZ and 27.0% in DZ, and the difference was statistically significant (P<0.001). The heritability of hypertension was 22.1% (95%CI: 16.3%- 28.0%). Stratified by gender, age, and region, the concordance rate of hypertension in MZ was still higher than that in DZ. The heritability of hypertension was higher in female participants. Conclusions: There were differences in the distribution of hypertension among twins with different demographic and regional characteristics. It is indicated that genetic factors play a crucial role in hypertension in different genders, ages, and regions, while the magnitude of genetic effects may vary.

[A descriptive analysis of hyperlipidemia in adult twins in China].

Objective: To describe the distribution characteristics of hyperlipidemia in adult twins in the Chinese National Twin Registry (CNTR) and explore the effect of genetic and environmental factors on hyperlipidemia. Methods: Twins recruited from the CNTR in 11 project areas across China were included in the study. A total of 69 130 (34 565 pairs) of adult twins with complete information on hyperlipidemia were selected for analysis. The random effect model was used to characterize the population and regional distribution of hyperlipidemia among twins. The concordance rates of hyperlipidemia were calculated in monozygotic twins (MZ) and dizygotic twins (DZ), respectively, to estimate the heritability. Results: The age of all participants was (34.2±12.4) years. This study's prevalence of hyperlipidemia was 1.3% (895/69 130). Twin pairs who were men, older, living in urban areas, married,had junior college degree or above, overweight, obese, insufficient physical activity, current smokers, ex-smokers, current drinkers, and ex-drinkers had a higher prevalence of hyperlipidemia (P<0.05). In within-pair analysis, the concordance rate of hyperlipidemia was 29.1% (118/405) in MZ and 18.1% (57/315) in DZ, and the difference was statistically significant (P<0.05). Stratified by gender, age, and region, the concordance rate of hyperlipidemia in MZ was still higher than that in DZ. Further, in within-same-sex twin pair analyses, the heritability of hyperlipidemia was 13.04% (95%CI: 2.61%-23.47%) in the northern group and 18.59% (95%CI: 4.43%-32.74%) in the female group, respectively. Conclusions: Adult twins were included in this study and were found to have a lower prevalence of hyperlipidemia than in the general population study, with population and regional differences. Genetic factors influence hyperlipidemia, but the genetic effect may vary with gender and area.

[Incidence and risk factors of acute mountain sickness in grid construction personnel working at plateau].

Objective: To analyze the incidence and risk factors of acute mountain sickness (AMS) in grid construction personnel working at plateau. Methods: A total of 10 956 plateau construction personnel of Ali Network Project from January 1, 2019 to December 31, 2020 were included. Baseline information (including age, sex, body mass index, developmental and nutritional status, relevant clinical indicators, etc.) and follow-up data of AMS were obtained from the medical record of Ali Internet engineering staff medical station. The altitude of the residence place in early life and the working environment were obtained from the website (https://zh-cn.topographic-map.com/legal/). The incidences of overall AMS and its subgroups were calculated, and the Cox proportional hazards model was used to explore the risk factors for AMS. Results: The age of the participants was (36.1±10.5) years old at baseline, and 95.27% (10 438) of them were males. The follow-up time was (17.46±4.23) months. The altitude of the residence place in early-life and working environment were (1 959±937) m and (4 533±233) m, respectively. During the follow-up period, the incidence of AMS was 15.58% (1 707 cases), and the incidence for acute mountain sickness and high altitude pulmonary edema were 15.53% (1 702 cases) and 0.05% (5 cases), respectively. No high altitude cerebral edema patients were found. Cox proportional hazards model showed that the risk of AMS increased by 45% for every 100 m elevation in the altitude of working environment [HR (95%CI): 1.45 (1.41-1.51)]. The higher the altitude for the residence place in early-life, the lower the risk of AMS [HR (95%CI): 0.84 (0.80-0.88)]. Compared with the group with oxygen saturation during 90%-94%, the participants with oxygen saturation<75% [HR (95%CI): 1.67 (1.24-2.23)] at baseline was also associated with increased risk of AMS. Conclusions: The incidence of AMS is relatively low in grid construction workers working on plateau. The risk factors of AMS included higher working altitude, lower altitude of the residence place in early-life and oxygen saturation<75%.

[Heritability and genetic correlation of body mass index and coronary heart disease in Chinese adult twins].

Objective: To examine the heritability of body mass index (BMI) and coronary heart disease (CHD), and to explore whether genetic factors can explain their correlation. Methods: Participants were from 11 provinces/municipalities reqistered in the Chinese National Twin Registry (CNTR) from 2010 to 2018. Participants data were collected from face-to-face questionnaire survey. Bivariate structure equation model was used to estimate the heritability and the genetic correlation of BMI and CHD. Results: A total of 20 340 pairs of same-sex twins aged ≥25 years were included in this study. After adjusting for age and gender, the heritability of BMI and CHD was 0.52 (95%CI: 0.49-0.55) and 0.76 (95%CI: 0.69-0.81), respectively. Further, a genetic correlation was identified between BMI and CHD (rA=0.10, 95%CI:0.02-0.17). Conclusion: In Chinese adult twin population, BMI and CHD are affected by genetic factors, and their correlation can be attributed to the common genetic basis.

[Gene-lifestyle interaction on coronary heart disease in adult twins of China].

Objective: To explore the gene-lifestyle interaction on coronary heart disease (CHD) in adult twins of China. Methods: Participants were selected from twin pairs registered in the Chinese National Twin Registry (CNTR). Univariate interaction model was used to estimate the interaction, via exploring the moderation effect of lifestyle on the genetic variance of CHD. Results: A total of 20 477 same-sex twin pairs aged ≥25 years were recruited, including 395 CHD cases, and 66 twin pairs both had CHD. After adjustment for age and sex, no moderation effects of lifestyles, including current smoking, current drinking, physical activity, intake of vegetable and fruit, on the genetic variance of CHD were found (P>0.05), suggesting no significant interactions. Conclusion: There was no evidence suggesting statistically significant gene-lifestyle interaction on CHD in adult twins of China.

[Methodology progress and challenges on assessing the appropriateness of real-world data].

From the perspective of data users, ensuring the relevance and reliability of big data in healthcare and medicine via assessments on data appropriateness is a prerequisite for generating high-quality real-world evidence, which could guarantee good representativeness and generalizability of real-world studies. This review summarized the quality dimensions, definitions, evaluation indexes and calculating methods of assessment on the appropriateness of real-world data (RWD) according to guidance from different countries and international organizations, as well as exploring the opportunities and challenges for better assessing RWD appropriateness.

[How to report adaptive design randomized trials-A interpretation of international reporting guideline ACE].

Concerns has been raised in improving the quality of adaptive design randomized trials reports. Based on the CONSORT 2010 (Consolidated Standards of Reporting Trials), The Adaptive designs CONSORT Extension (ACE) has developed items and reporting specifications for adaptive design trials. This paper presents a brief explanation of the extension and new items of ACE and introduces the applications of ACE checklist with examples.